System

Abstract

An object of a system according to an embodiment is to allow a user to select an optimal matching service and automatically generate an appropriate profile or message.SOLUTION: A system according to an embodiment includes a matching service selection unit, a profile creation support unit, and an automatic message generation unit. The matching service selection unit proposes an optimal matching service on the basis of the user's preferences and past usage history. The profile creation support unit automatically creates a profile based on the basic information, hobbies, and interests of the user. The automatic message generation unit automatically generates a message on the basis of a user's intention or emotion.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of making it difficult for users to select the most suitable matching service and create appropriate profiles and messages.

[0005] The system according to the embodiment aims to allow users to select the most suitable matching service and automatically generate an appropriate profile and message. [Means for solving the problem]

[0006] The system according to the embodiment includes a matching service selection unit, a profile creation support unit, and an automatic message generation unit. The matching service selection unit suggests the most suitable matching service based on the user's preferences and past usage history. The profile creation support unit automatically generates a profile based on the user's basic information, hobbies, and interests. The automatic message generation unit automatically generates a message based on the user's intentions and emotions. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to select the most suitable matching service and automatically generate an appropriate profile and message. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The matching integrated system according to an embodiment of the present invention is a system that integrates an electronic payment service with a matching service for single men and women, and uses a generation AI to propose the most suitable matching service based on the user's preferences and past usage history, automatically generates a profile, and automates message exchanges. As a result, the matching integrated system reduces the burden on users and allows them to use the matching service more comfortably.

[0029] The matching integrated system according to the embodiment includes a matching service selection unit, a profile creation support unit, and an automatic message generation unit. The matching service selection unit suggests an optimal matching service based on the user's preferences and past usage history. For example, the generation AI recommends an optimal matching service based on the user's past usage history and preferences. The generation AI can also analyze the user's usage history and suggest optimal services. The profile creation support unit automatically generates a profile based on the user's basic information, hobbies, and interests. For example, the generation AI generates an attractive profile sentence based on the hobbies and interests entered by the user. The generation AI can also automatically generate a profile based on the user's basic information. The automatic message generation unit automatically generates a message based on the user's intentions and emotions. For example, if the user wants to send a message such as "Hello, how are you lately?", the generation AI analyzes the user's intention and automatically generates a reply such as "Hello! I've been busy lately, but I'm doing well. How about you?" The generation AI can also analyze the user's emotions and generate an appropriate message. This allows the matching integrated system according to the embodiment to reduce the user's burden and enable more comfortable use of the matching service. For example, users can efficiently find a partner by selecting the best matching service, creating an attractive profile, and automating message exchanges.

[0030] The matching service selection unit analyzes the content of a user's past messages and conversation history to extract more detailed preferences and interests and suggest the most suitable matching service. For example, the matching service selection unit uses a generation AI to analyze the content of a user's past messages and extract specific keywords and phrases. For example, the unit suggests the most suitable matching service based on the hobbies and interests that the user frequently talks about. The matching service selection unit also analyzes conversation history to identify the type of people the user often talks to. For example, the unit recommends the most suitable matching service based on the characteristics of people with whom the user has exchanged many messages in the past. The matching service selection unit also analyzes the user's emotions and tone from the message content to extract more detailed preferences and interests. For example, the unit suggests the most suitable matching service based on topics that elicit positive emotions. In this way, by analyzing the content of a user's past messages and conversation history, the unit can extract more detailed preferences and interests and suggest the most suitable matching service.

[0031] The matching service selection unit can analyze a user's social media activity and recommend the optimal matching service based on their online behavioral patterns. For example, the matching service selection unit uses a generation AI to analyze a user's social media accounts and identify preferences based on the content of posts and trends in likes. For example, the matching service selection unit suggests the optimal matching service based on the content of posts that the user frequently likes. The matching service selection unit also analyzes the characteristics of friends and followers on social media to identify the type of community the user belongs to. For example, it recommends matching services that are frequently used by people in the same community. The matching service selection unit also analyzes the time and frequency of a user's social media activity to understand their online behavioral patterns. For example, it suggests matching services that are active at night to a user who is active at night. In this way, by analyzing a user's social media activity, the optimal matching service can be recommended based on their online behavioral patterns.

[0032] The profile creation support unit can analyze a user's past photos and videos and automatically generate a visually appealing profile. In the profile creation support unit, for example, a generation AI analyzes a user's past photos and automatically generates a visually appealing profile image. For example, it selects photos of the user smiling and proposes them as profile images. The profile creation support unit also analyzes a user's videos and automatically generates a visually appealing profile video. For example, it extracts scenes of the user talking happily and proposes them as profile videos. In addition, the profile creation support unit analyzes a user's photos and videos and automatically generates a visually appealing profile. For example, it selects photos and videos that reflect the user's hobbies and interests and incorporates them into the profile. In this way, a visually appealing profile can be automatically generated by analyzing a user's past photos and videos.

[0033] The profile creation support unit can analyze the content of a user's past messages and generate an attractive profile statement using natural language. For example, the profile creation support unit uses a generation AI to analyze the content of a user's past messages and generate an attractive profile statement using natural language. For example, it proposes a profile statement that incorporates phrases and expressions that the user frequently uses. The profile creation support unit also analyzes the content of a user's messages and generates an attractive profile statement using natural language. For example, it proposes a profile statement that reflects the user's hobbies and interests. The profile creation support unit also analyzes the content of a user's past messages and generates an attractive profile statement using natural language. For example, it proposes a profile statement that reflects the user's personality and values. In this way, by analyzing the content of a user's past messages, it is possible to generate an attractive profile statement using natural language.

[0034] The automatic message generation unit can analyze a user's past message history and generate personalized messages tailored to each individual recipient. In the automatic message generation unit, for example, a generation AI analyzes a user's past message history and generates personalized messages tailored to a specific recipient. For example, it may suggest messages based on the recipient's hobbies and interests. The automatic message generation unit also generates personalized messages that reflect the recipient's preferences and interests based on the user's message history. For example, it may suggest messages that incorporate the recipient's favorite topics. In addition, the automatic message generation unit also analyzes a user's past message history and generates personalized messages tailored to each recipient. For example, it may predict the recipient's reaction and suggest an appropriate reply. In this way, by analyzing a user's past message history, it is possible to generate personalized messages tailored to each individual recipient.

[0035] The automatic message generation unit can generate messages containing humor or interesting topics based on the content of the user's past messages. In the automatic message generation unit, for example, the generation AI analyzes the content of the user's past messages and generates messages containing humor or interesting topics. For example, it suggests messages that incorporate jokes or interesting topics that the user has used in the past. The automatic message generation unit also generates messages containing humor or interesting topics based on the content of the user's messages. For example, it suggests messages that incorporate topics that the user is interested in. In addition, the automatic message generation unit analyzes the content of the user's past messages and generates messages containing humor or interesting topics. For example, it suggests messages that incorporate interesting episodes that the user has talked about in the past. In this way, it is possible to generate messages containing humor or interesting topics based on the content of the user's past messages.

[0036] The automatic message generation unit can analyze the user's past message history, predict the other person's reaction, and generate the optimal reply. In the automatic message generation unit, for example, the generation AI analyzes the user's past message history, predicts the other person's reaction, and generates the optimal reply. For example, it suggests a message that the other person will react positively to. The automatic message generation unit also predicts the other person's reaction based on the user's message history and generates the optimal reply. For example, it suggests a reply that incorporates topics that the other person is likely to be interested in. In addition, the automatic message generation unit can analyze the user's past message history, predict the other person's reaction, and generate the optimal reply. For example, it suggests a message that the other person will react favorably to. In this way, the automatic message generation unit can analyze the user's past message history, predict the other person's reaction, and generate the optimal reply.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The integrated matching system can also analyze a user's health data and suggest optimal matching services based on their health status. For example, it can recommend partners who share a healthy lifestyle based on data obtained from the user's fitness tracker or smartwatch. It can also analyze the user's sleep patterns and exercise habits to suggest partners with similar lifestyles. It can also analyze the user's food records to recommend partners with similar eating preferences. This allows the system to utilize the user's health data to find a healthier and more compatible partner.

[0039] The integrated matching system can also analyze a user's travel history and suggest the most suitable matching service based on their travel preferences. For example, it can recommend people who like to travel based on the places the user has visited in the past and the frequency of their travels. It can also analyze the user's preferred travel style (e.g., adventure travel or resort trips) and suggest people who like the same style. It can also analyze the user's activities during travel (e.g., hiking or sightseeing) and recommend people who share common interests. This makes it possible to use the user's travel history to find partners with similar travel preferences.

[0040] The integrated matching system can also analyze a user's music streaming service history and suggest optimal matching services based on their music preferences. For example, it can recommend partners with similar musical tastes based on the artists and genres the user frequently listens to. It can also analyze the user's history of concerts and events and suggest partners who have attended the same events. It can also analyze the user's playlists and recommend partners who like similar songs and artists. This allows users to use their music streaming history to find partners with similar musical tastes.

[0041] The integrated matching system can also analyze a user's reading history and suggest optimal matching services based on their reading preferences. For example, it can recommend partners with similar reading tastes based on the books and authors the user has read in the past. It can also analyze the user's preferred genres (e.g., mysteries or romance) and suggest partners who like the same genres. It can also analyze the user's reading pace and reading time to recommend partners with similar reading habits. This makes it possible to use the user's reading history to find partners with similar reading tastes.

[0042] The integrated matching system can also analyze a user's movie viewing history and suggest optimal matching services based on their movie preferences. For example, it can recommend partners with similar movie tastes based on the movies and directors the user has watched in the past. It can also analyze the user's preferred movie genres (e.g., action or drama) and suggest partners who like the same genre. It can also analyze the user's movie viewing frequency and viewing time to recommend partners with similar viewing habits. This makes it possible to use a user's movie viewing history to find a partner with similar movie tastes.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The matching service selection unit proposes the optimal matching service based on the user's preferences and past usage history. For example, the generation AI recommends the optimal matching service based on the user's past usage of services and their preferences. The generation AI can also analyze the user's usage history to propose the optimal service. Step 2: The profile creation support unit automatically generates a profile based on the user's basic information, hobbies, and interests. For example, the generation AI generates an appealing profile statement based on the hobbies and interests entered by the user. The generation AI can also automatically generate a profile based on the user's basic information. Step 3: The automatic message generation unit automatically generates a message based on the user's intentions and emotions. For example, if a user wants to send a message such as "Hello, how are you doing lately?", the generation AI analyzes the user's intention and automatically generates a reply such as "Hello! I've been busy lately, but I'm doing well. How are you?" The generation AI can also analyze the user's emotions and generate an appropriate message.

[0045] (Example 2) The matching integrated system according to an embodiment of the present invention is a system that integrates an electronic payment service with a matching service for single men and women, and uses a generation AI to propose the most suitable matching service based on the user's preferences and past usage history, automatically generates a profile, and automates message exchanges. As a result, the matching integrated system reduces the burden on users and allows them to use the matching service more comfortably.

[0046] The matching integrated system according to the embodiment includes a matching service selection unit, a profile creation support unit, and an automatic message generation unit. The matching service selection unit suggests an optimal matching service based on the user's preferences and past usage history. For example, the generation AI recommends an optimal matching service based on the user's past usage history and preferences. The generation AI can also analyze the user's usage history and suggest optimal services. The profile creation support unit automatically generates a profile based on the user's basic information, hobbies, and interests. For example, the generation AI generates an attractive profile sentence based on the hobbies and interests entered by the user. The generation AI can also automatically generate a profile based on the user's basic information. The automatic message generation unit automatically generates a message based on the user's intentions and emotions. For example, if the user wants to send a message such as "Hello, how are you lately?", the generation AI analyzes the user's intention and automatically generates a reply such as "Hello! I've been busy lately, but I'm doing well. How about you?" The generation AI can also analyze the user's emotions and generate an appropriate message. This allows the matching integrated system according to the embodiment to reduce the user's burden and enable more comfortable use of the matching service. For example, users can efficiently find a partner by selecting the best matching service, creating an attractive profile, and automating message exchanges.

[0047] The matching service selection unit analyzes the content of a user's past messages and conversation history to extract more detailed preferences and interests and suggest the most suitable matching service. For example, the matching service selection unit uses a generation AI to analyze the content of a user's past messages and extract specific keywords and phrases. For example, the unit suggests the most suitable matching service based on the hobbies and interests that the user frequently talks about. The matching service selection unit also analyzes conversation history to identify the type of people the user often talks to. For example, the unit recommends the most suitable matching service based on the characteristics of people with whom the user has exchanged many messages in the past. The matching service selection unit also analyzes the user's emotions and tone from the message content to extract more detailed preferences and interests. For example, the unit suggests the most suitable matching service based on topics that elicit positive emotions. In this way, by analyzing the content of a user's past messages and conversation history, the unit can extract more detailed preferences and interests and suggest the most suitable matching service.

[0048] The matching service selection unit can analyze a user's social media activity and recommend the optimal matching service based on their online behavioral patterns. For example, the matching service selection unit uses a generation AI to analyze a user's social media accounts and identify preferences based on the content of posts and trends in likes. For example, the matching service selection unit suggests the optimal matching service based on the content of posts that the user frequently likes. The matching service selection unit also analyzes the characteristics of friends and followers on social media to identify the type of community the user belongs to. For example, it recommends matching services that are frequently used by people in the same community. The matching service selection unit also analyzes the time and frequency of a user's social media activity to understand their online behavioral patterns. For example, it suggests matching services that are active at night to a user who is active at night. In this way, by analyzing a user's social media activity, the optimal matching service can be recommended based on their online behavioral patterns.

[0049] The matching service selection unit can use the emotion estimation function to analyze the user's emotional reactions to matching services that they have used in the past and suggest services that elicit positive emotions. For example, the matching service selection unit uses a generation AI to analyze the user's past history of using matching services and the emotion estimation function to identify services that elicit positive emotions. For example, the matching service selection unit suggests the optimal matching service based on services that the user has given high ratings. The matching service selection unit also uses the emotion estimation function to analyze the user's emotional reactions to matching services that they have used in the past. For example, the matching service selection unit recommends the optimal service based on messages and reviews in which the user expressed positive emotions. The matching service selection unit also identifies matching services that elicit positive emotions based on the user's emotional reaction data. For example, the matching service selection unit suggests the service that elicited the most positive emotions among the services the user has used in the past. In this way, the emotion estimation function can be used to analyze the user's emotional reactions to matching services that they have used in the past and suggest services that elicit positive emotions.

[0050] The profile creation support unit can analyze a user's past photos and videos and automatically generate a visually appealing profile. In the profile creation support unit, for example, a generation AI analyzes a user's past photos and automatically generates a visually appealing profile image. For example, it selects photos of the user smiling and proposes them as profile images. The profile creation support unit also analyzes a user's videos and automatically generates a visually appealing profile video. For example, it extracts scenes of the user talking happily and proposes them as profile videos. In addition, the profile creation support unit analyzes a user's photos and videos and automatically generates a visually appealing profile. For example, it selects photos and videos that reflect the user's hobbies and interests and incorporates them into the profile. In this way, a visually appealing profile can be automatically generated by analyzing a user's past photos and videos.

[0051] The profile creation support unit can analyze the content of a user's past messages and generate an attractive profile statement using natural language. For example, the profile creation support unit uses a generation AI to analyze the content of a user's past messages and generate an attractive profile statement using natural language. For example, it proposes a profile statement that incorporates phrases and expressions that the user frequently uses. The profile creation support unit also analyzes the content of a user's messages and generates an attractive profile statement using natural language. For example, it proposes a profile statement that reflects the user's hobbies and interests. The profile creation support unit also analyzes the content of a user's past messages and generates an attractive profile statement using natural language. For example, it proposes a profile statement that reflects the user's personality and values. In this way, by analyzing the content of a user's past messages, it is possible to generate an attractive profile statement using natural language.

[0052] The profile creation support unit can use the emotion estimation function to make suggestions to reduce stress and anxiety felt by the user while creating a profile. The profile creation support unit, for example, uses the emotion estimation function to analyze the stress and anxiety felt by the user while creating a profile in real time. For example, if the user is feeling stressed, the profile creation support unit makes suggestions to help the user relax. The profile creation support unit also analyzes the user's emotions in real time and makes suggestions to reduce stress and anxiety. For example, if the user is feeling anxious, the profile creation support unit provides advice that gives the user a sense of security. The profile creation support unit also uses the emotion estimation function to analyze the stress and anxiety felt by the user while creating a profile and makes suggestions to reduce the stress and anxiety. For example, if the user is feeling nervous, the profile creation support unit suggests ways to help the user relax. In this way, by using the emotion estimation function, suggestions can be made to reduce the stress and anxiety felt by the user while creating a profile.

[0053] The automatic message generation unit can analyze a user's past message history and generate personalized messages tailored to each individual recipient. In the automatic message generation unit, for example, a generation AI analyzes a user's past message history and generates personalized messages tailored to a specific recipient. For example, it may suggest messages based on the recipient's hobbies and interests. The automatic message generation unit also generates personalized messages that reflect the recipient's preferences and interests based on the user's message history. For example, it may suggest messages that incorporate the recipient's favorite topics. In addition, the automatic message generation unit also analyzes a user's past message history and generates personalized messages tailored to each recipient. For example, it may predict the recipient's reaction and suggest an appropriate reply. In this way, by analyzing a user's past message history, it is possible to generate personalized messages tailored to each individual recipient.

[0054] The automatic message generation unit can use the emotion estimation function to make suggestions to reduce stress and anxiety felt by the user while exchanging messages. The automatic message generation unit, for example, uses the emotion estimation function to analyze the stress and anxiety felt by the user while exchanging messages in real time. For example, if the user is feeling stressed, the automatic message generation unit makes suggestions to help the user relax. The automatic message generation unit also analyzes the user's emotions in real time and makes suggestions to reduce stress and anxiety. For example, if the user is feeling anxious, the automatic message generation unit provides advice that gives the user a sense of security. The automatic message generation unit also uses the emotion estimation function to analyze the stress and anxiety felt by the user while exchanging messages and makes suggestions to reduce the stress and anxiety. For example, if the user is feeling nervous, the automatic message generation unit suggests ways to help the user relax. In this way, by using the emotion estimation function, suggestions can be made to reduce the stress and anxiety felt by the user while exchanging messages.

[0055] The automatic message generation unit can generate messages containing humor or interesting topics based on the content of the user's past messages. In the automatic message generation unit, for example, the generation AI analyzes the content of the user's past messages and generates messages containing humor or interesting topics. For example, it suggests messages that incorporate jokes or interesting topics that the user has used in the past. The automatic message generation unit also generates messages containing humor or interesting topics based on the content of the user's messages. For example, it suggests messages that incorporate topics that the user is interested in. In addition, the automatic message generation unit analyzes the content of the user's past messages and generates messages containing humor or interesting topics. For example, it suggests messages that incorporate interesting episodes that the user has talked about in the past. In this way, it is possible to generate messages containing humor or interesting topics based on the content of the user's past messages.

[0056] The automatic message generation unit can analyze the user's past message history, predict the other person's reaction, and generate the optimal reply. In the automatic message generation unit, for example, the generation AI analyzes the user's past message history, predicts the other person's reaction, and generates the optimal reply. For example, it suggests a message that the other person will react positively to. The automatic message generation unit also predicts the other person's reaction based on the user's message history and generates the optimal reply. For example, it suggests a reply that incorporates topics that the other person is likely to be interested in. In addition, the automatic message generation unit can analyze the user's past message history, predict the other person's reaction, and generate the optimal reply. For example, it suggests a message that the other person will react favorably to. In this way, the automatic message generation unit can analyze the user's past message history, predict the other person's reaction, and generate the optimal reply.

[0057] The automatic message generation unit can use the emotion estimation function to make suggestions to enhance the positive emotions felt by the user while exchanging messages. The automatic message generation unit, for example, uses the emotion estimation function to analyze the positive emotions felt by the user while exchanging messages in real time. For example, if the user is enjoying themselves, the automatic message generation unit makes suggestions to enhance those emotions. The automatic message generation unit also analyzes the user's emotions in real time and makes suggestions to enhance the positive emotions. For example, if the user is feeling happy, the automatic message generation unit provides advice to further enhance those emotions. The automatic message generation unit also uses the emotion estimation function to analyze the positive emotions felt by the user while exchanging messages and makes suggestions to enhance them. For example, if the user is feeling happy, the automatic message generation unit suggests a way to maintain those emotions. In this way, the emotion estimation function can be used to make suggestions to enhance the positive emotions felt by the user while exchanging messages.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The integrated matching system can also analyze a user's health data and suggest optimal matching services based on their health status. For example, it can recommend partners who share a healthy lifestyle based on data obtained from the user's fitness tracker or smartwatch. It can also analyze the user's sleep patterns and exercise habits to suggest partners with similar lifestyles. It can also analyze the user's food records to recommend partners with similar eating preferences. This allows the system to utilize the user's health data to find a healthier and more compatible partner.

[0060] The integrated matching system can also analyze a user's travel history and suggest the most suitable matching service based on their travel preferences. For example, it can recommend people who like to travel based on the places the user has visited in the past and the frequency of their travels. It can also analyze the user's preferred travel style (e.g., adventure travel or resort trips) and suggest people who like the same style. It can also analyze the user's activities during travel (e.g., hiking or sightseeing) and recommend people who share common interests. This makes it possible to use the user's travel history to find partners with similar travel preferences.

[0061] The integrated matching system can also analyze a user's music streaming service history and suggest optimal matching services based on their music preferences. For example, it can recommend partners with similar musical tastes based on the artists and genres the user frequently listens to. It can also analyze the user's history of concerts and events and suggest partners who have attended the same events. It can also analyze the user's playlists and recommend partners who like similar songs and artists. This allows users to use their music streaming history to find partners with similar musical tastes.

[0062] The integrated matching system can also analyze a user's reading history and suggest optimal matching services based on their reading preferences. For example, it can recommend partners with similar reading tastes based on the books and authors the user has read in the past. It can also analyze the user's preferred genres (e.g., mysteries or romance) and suggest partners who like the same genres. It can also analyze the user's reading pace and reading time to recommend partners with similar reading habits. This makes it possible to use the user's reading history to find partners with similar reading tastes.

[0063] The integrated matching system can also analyze a user's movie viewing history and suggest optimal matching services based on their movie preferences. For example, it can recommend partners with similar movie tastes based on the movies and directors the user has watched in the past. It can also analyze the user's preferred movie genres (e.g., action or drama) and suggest partners who like the same genre. It can also analyze the user's movie viewing frequency and viewing time to recommend partners with similar viewing habits. This makes it possible to use a user's movie viewing history to find a partner with similar movie tastes.

[0064] The integrated matching system can also estimate the user's emotions and provide a relaxing environment for the user based on the estimated emotions. For example, if the user is feeling stressed, it can provide relaxing music or videos. If the user is feeling anxious, it can provide messages or advice that give a sense of security. Furthermore, if the user is expressing positive emotions, it can provide content that further enhances those emotions. This provides an environment that corresponds to the user's emotions, making it possible to use the matching service more comfortably.

[0065] The integrated matching system can also estimate the user's emotions and suggest events and activities that the user might be interested in based on the estimated emotions. For example, if the user is having fun, it can suggest events that will further enhance that emotion. If the user is bored, it can suggest activities that will pique the user's interest. Furthermore, if the user is relaxed, it can suggest relaxing events to maintain that state. This allows the system to suggest events and activities that match the user's emotions, allowing the user to spend a more fulfilling time.

[0066] The integrated matching system can also estimate the user's emotions and suggest hobbies and activities that the user might be interested in based on the estimated emotions. For example, if the user is expressing positive emotions, the system can suggest hobbies and activities that will further enhance those emotions. If the user is feeling stressed, the system can suggest hobbies and activities that will help them relax. Furthermore, if the user is bored, the system can suggest new hobbies and activities that will pique the user's interest. This allows the system to suggest hobbies and activities that match the user's emotions, allowing the user to spend more fulfilling time.

[0067] The integrated matching system can also estimate the user's emotions and suggest places and environments where the user can relax based on the estimated emotions. For example, if the user is feeling stressed, it can suggest a relaxing cafe or park. If the user is feeling anxious, it can also suggest places and environments that will give the user a sense of security. Furthermore, if the user is expressing positive emotions, it can also suggest places and environments that will further enhance those emotions. This allows the system to suggest places and environments that correspond to the user's emotions, allowing the user to spend their time more comfortably.

[0068] The matching integrated system can also estimate the user's emotions and provide music and videos that help the user relax based on the estimated emotions. For example, if the user is feeling stressed, it can provide relaxing music. If the user is feeling anxious, it can provide videos that give a sense of security. Furthermore, if the user is showing positive emotions, it can provide music and videos that further enhance those emotions. This allows the user to enjoy music and videos that match their emotions, making their time more comfortable.

[0069] The processing flow of the second embodiment will be briefly explained below.

[0070] Step 1: The matching service selection unit proposes the optimal matching service based on the user's preferences and past usage history. For example, the generation AI recommends the optimal matching service based on the user's past usage of services and their preferences. The generation AI can also analyze the user's usage history to propose the optimal service. Step 2: The profile creation support unit automatically generates a profile based on the user's basic information, hobbies, and interests. For example, the generation AI generates an appealing profile statement based on the hobbies and interests entered by the user. The generation AI can also automatically generate a profile based on the user's basic information. Step 3: The automatic message generation unit automatically generates a message based on the user's intentions and emotions. For example, if a user wants to send a message such as "Hello, how are you doing lately?", the generation AI analyzes the user's intention and automatically generates a reply such as "Hello! I've been busy lately, but I'm doing well. How are you?" The generation AI can also analyze the user's emotions and generate an appropriate message.

[0071] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0072] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0073] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0074] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0075] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0076] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0077] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0078] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0079] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0080] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0081] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0082] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0083] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0084] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0085] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0086] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0087] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0088] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0089] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0090] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0091] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0092] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0093] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0094] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0095] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0096] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0097] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0099] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0100] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0101] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0104] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0105] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0112] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0121] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0122] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0123] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0124] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0125] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0126] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0127] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0128] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0129] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0130] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0131] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0132] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0133] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0134] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0135] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0136] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0137] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0138] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system equipped with a generative AI, The generated AI is a matching service selection unit that proposes an optimal matching service based on the user's preferences and past usage history; a profile creation support unit that automatically creates a profile based on the user's basic information, hobbies, and interests; an automatic message generation unit that automatically generates a message based on the user's intentions and feelings; A system characterized by:

2. The matching service selection unit The content of the user's past messages and conversation history are analyzed, and more detailed preferences and interests are extracted to propose the most suitable matching service.

2. The system of claim 1.

3. The matching service selection unit Analyzing the user's social media activity and recommending the most suitable matching service based on their online behavioral patterns 2. The system of claim 1.

4. The matching service selection unit Analyzing the user's emotional response to the matching service that the user has used in the past, and proposing a service that elicits positive emotions 2. The system of claim 1.

5. The profile creation support unit Analyzing the user's past photos and videos to automatically generate a visually appealing profile 2. The system of claim 1.

6. The profile creation support unit Analyze the content of the user's past messages and generate an attractive profile using natural language.

2. The system of claim 1.

7. The profile creation support unit making suggestions to reduce stress or anxiety experienced by the user while creating their profile; 2. The system of claim 1.

8. The automatic message generation unit Analyzing the user's past message history and generating personalized messages tailored to each individual recipient 2. The system of claim 1.

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